In recent years, biologists and clinicians have gained access to unprecedented amounts of imaging data, depicting static and dynamic processes in cells and tissues. While this trove hides answers to a host of important questions, mining it visually, as it is typically done, is an enormous and error-prone task wasting valuable resources. As such, the automation of this processing has become an important area of emerging research. Classification, a standard task in image processing, underlies many problems in medicine and biology, such as recognizing proteins based on their subcellular location patterns, determination of developmental stages in Drosophila embryos, recognizing tissues in histology and diagnosis of otitis media. Thus: We propose to develop a flexible, modular and accurate algorithm and a software toolbox to automatically recognize and identify normal and pathological processes occurring in disease and development. A generic classification system computes a set of numerical features describing the data, followed by separating these features into classes. We propose to decompose the image first using a multiresolution transform, as we postulate that multiresolution subspaces hide valuable information. Each subspace performs separate classification, giving its vote. The arbiter reconciling these local votes into a single, global one, is the weighting block. It assigns a weight to each subspace based on how reliable its voting has been during training. Based on our preliminary work, we believe this system to have great potential for accurate and robust classification (recognition, identification) of normal and pathological processes occurring in disease and development. Specific Aim 1: Develop a classification algorithm based on multiresolution transforms, that is flexible, modular and accurate, and has an efficient implementation. Specific Aim 2: Develop a flexible classification software platform and a user-friendly GUI to facilitate both use by biologists and clinicians, as well as their interaction with algorithm developers. Significance of the Proposed Work: The flexibility and modularity of the proposed system together with features developed for our three testbeds will allow for a broad use in a wide range of applications within the broad hierarchy of organ development. The distribution of the software as an open-source ImageJ plugin will allow for its wide use in the biological and medical communities. Innovation the Proposed Work Brings. The algorithm we propose is flexible, accurate and novel: multiresolution tools offer a window into previously unseen features within a dataset. Each block of the multiresolution classifier will offer a novel contribution: (1) construction of frame families in the multiresolution block, (2) novel features in the feature extractor block, (3) multiresolution versions of known classifiers in the classifier block. Moreover, the testbeds we consider do not have an available tool for automated classification. PUBLIC HEALTH RELEVANCE: Narrative The motivation is for this algorithm and software toolbox to be available to the biological and medical communities for mining imaging data. As our three testbeds span various scales within the broad hierarchy of organ development, the success of our system will bring advances both in basic research at molecular and cellular levels (Drosophila project) as well as at tissue and organ levels (histology and otitis media projects).